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Forecasting oil prices with random forests

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  • Emanuel Kohlscheen

    (Bank for International Settlements)

Abstract

This study analyzes oil price movements through the lens of an agnostic random forest model, which is based on 1000 regression trees. It shows that this highly disciplined, yet flexible computational model reduces in-sample root-mean-square errors (RMSEs) by 65% relative to a standard linear least square model that uses the same comprehensive set of 11 high-frequency explanatory factors. In 1–3 months ahead price forecasting exercises the RMSE reduction relative to OLS ranges exceeds 50%, highlighting the relevance of non-linearities in oil markets. The results underscore the importance of incorporating financial factors into oil models: US interest rates, the dollar and the VIX together account for 39% of the models’ RMSE reduction in the post-2010 sample, rising to 48% in the post-2020 sample.

Suggested Citation

  • Emanuel Kohlscheen, 2024. "Forecasting oil prices with random forests," Empirical Economics, Springer, vol. 66(2), pages 927-943, February.
    • Handle: RePEc:spr:empeco:v:66:y:2024:i:2:d:10.1007_s00181-023-02480-0
    DOI: 10.1007/s00181-023-02480-0
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    References listed on IDEAS

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    More about this item

    Keywords

    Dollar; Forecasting; Machine learning; Oil; Risk;
    All these keywords.

    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • F30 - International Economics - - International Finance - - - General
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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